metadata

license: llama3.1
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - Nexesenex/Llama_3.1_8b_Smarteaz_0.21_R1
  - Nexesenex/Llama_3.1_8b_Smarteaz_0.11a
  - Nexesenex/Llama_3.1_8b_Smarteaz_0.21_SN
model-index:
  - name: Llama_3.1_8b_Smarteaz_V1.01
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 81.51
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 32.28
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 23.41
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 7.94
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 8.2
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 30.4
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Nexesenex/Llama_3.1_8b_Smarteaz_V1.01
          name: Open LLM Leaderboard

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using Nexesenex/Llama_3.1_8b_Smarteaz_0.11a as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: model_stock
models:
  - model: Nexesenex/Llama_3.1_8b_Smarteaz_0.21_R1
    parameters:
      weight: 1.0
  - model: Nexesenex/Llama_3.1_8b_Smarteaz_0.21_SN
    parameters:
      weight: 1.0
base_model: Nexesenex/Llama_3.1_8b_Smarteaz_0.11a
dtype: bfloat16
normalize: true
chat_template: auto
tokenizer:
  source: union

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.62
IFEval (0-Shot)	81.51
BBH (3-Shot)	32.28
MATH Lvl 5 (4-Shot)	23.41
GPQA (0-shot)	7.94
MuSR (0-shot)	8.20
MMLU-PRO (5-shot)	30.40